Zhuoyang Qian, Biao Wu +7Affiliation: Vast Intelligence Lab
University of Technology Sydney, Affiliation: Equal Contribution
Corresponding Authorhttps://github.com/Spark-To-Paper-Skills/[email protected]
Generates full publication-format research papers from a short idea by composing 13 coding-assistant skills; it retrieves literature, plans and runs feasible experiments, produces editable vector figures, and enforces deterministic integrity checks so claims are revised to match measured evidence.
AI Agent Papers2026
Bobo Li, Hao Fei +31National University of Singapore 2University of Oxford, Project page: https://omni-scientist.github.io +1
Conducts end-to-end multidisciplinary research directly from heterogeneous raw evidence using lifecycle-wide perception and three autonomous agents (Ideation, Experiment, Writeup). Integrates perceptual analysis, execution provenance, and code-enforced checks to produce executable analyses, validated results, and compiled manuscripts across many modalities.
AI Agent Papers2026
Turns natural-language PLC requirements into verified, runnable IEC 61131-3 Structured Text by driving a closed loop of generation, compilation, deployment, and behavioral verification on a live OpenPLC runtime. The verification-gated harness forces inputs, traces execution, repairs failures, and renders ladder diagrams plus process simulation to raise dynamic runtime pass rates.
Adapts off-policy RL stabilizers to the available data regime: introduces WarpSAC, a regime-aware family using Sample Weight Decay plus two regime-matched variants (WarpSAC-L and WarpSAC-A) to improve sample efficiency, wall-time learning, and sim-to-real deployment.
Generates unified embeddings for text, images, video, visual documents and interleaved multimodal inputs with configurable output dimensions and Matryoshka truncation to trade accuracy for cost. Model weights and code are released under Apache-2.0; the 9B variant scores 80.6 on MMEB-v2.
Performs causal, bounded‑memory streaming 3D reconstruction by caching KV features from only the preceding 11 frames, predicting a per‑frame point map and adjacent relative pose, and composing these local predictions into a global trajectory; includes a lightweight rotation refiner and composition‑aware loss to limit drift.
AI Agent Papers2026
Trains LLM agents to proactively edit and manage their working context for long-horizon tasks using an expanded toolset (planning, long-term memory, soft offloading) and a fine-grained RL algorithm that identifies critical edits and assigns action-level credit. Improves accuracy while keeping contexts compact on long-context QA and deep search.
Turns sparse per-student records into individualized simulators that both reproduce a student’s responses and update them under tutor guidance using pooled LLM pretraining followed by per-student specialization; releases StudentSimEval and reference simulators across chess, L2 writing, and math.
Compresses conversational histories and long documents into short sequences of continuous soft memory tokens that a frozen decoder can read directly without text reconstruction. Uses a small reader-matched writer that trains only a tiny adapter, achieving 4–16× compression and much faster write/read latencies.
AI Agent Papers2026
Jianlyu Chen, Yuyang Hu +9Beijing Academy of Artificial Intelligence, University of Science and Technology of China +2
Distills operational know‑how from ML GitHub repositories into compact, verified 'skills' that research agents can load and reuse. Produces a skill format (SKILL.md, references, scripts), the AREX‑Skill Library (5,000+ skills from 1,000 repos), and demonstrates sizable benchmark gains when agents use skills.
Zixuan Fu, Bingxiang He +11Affiliation: University of Chinese Academy of Sciences, Affiliation: Tsinghua University +3
Studies on-policy distillation (OPD) at the data-minimal limit by training on a single query, measuring state coverage and alignment dynamics, and showing OPD is often data-overfed but algorithm-starved.
Compresses KV cache for long-chain reasoning by keeping prompt tokens and evicting remaining entries uniformly at random per attention head; across four models and six reasoning tasks it matches the strongest prior evictor while delivering 32–43% higher vLLM throughput. Relies on prompt protection and redundancy across heads/text to retain reasoning traces; suitable when static memory budgets and higher serving throughput are priorities.